SAR Image Speckle Reduction Based on Nonconvex Hybrid Total Variation Model

被引:24
|
作者
Sun, Yuli [1 ]
Lei, Lin [1 ]
Guan, Dongdong [2 ]
Li, Xiao [1 ]
Kuang, Gangyao [1 ]
机构
[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China
[2] High Tech Inst Xian, Xian 710025, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2021年 / 59卷 / 02期
基金
中国国家自然科学基金;
关键词
TV; Radar polarimetry; Speckle; Synthetic aperture radar; Image edge detection; Transforms; Scattering; High-order total variation (TV); < italic xmlns:ali="http:; www; niso; org; schemas; ali; 1; 0; xmlns:mml="http:; w3; 1998; Math; MathML" xmlns:xlink="http:; 1999; xlink" xmlns:xsi="http:; 2001; XMLSchema-instance"> l?<; italic >-norm; speckle; synthetic aperture radar (SAR) image; WAVELET SHRINKAGE; SEGMENTATION; RESTORATION; FRAMEWORK; CLASSIFICATION; DECOMPOSITION;
D O I
10.1109/TGRS.2020.3002561
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ${\ell _{1}}$ -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ${\ell _{1}}$ -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called FisherTippett (FT) distribution-${\ell _{p}}$ -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method.
引用
收藏
页码:1231 / 1249
页数:19
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